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    المساهمون: Analytical Chemistry and Forensic Science (HIMS, FNWI), Pathology, Nephrology, ACS - Diabetes & metabolism, APH - Digital Health, APH - Global Health, Center of Experimental and Molecular Medicine, Epidemiology and Data Science, APH - Aging & Later Life, ACS - Pulmonary hypertension & thrombosis

    المصدر: The Lancet Digital Health, 4(1), 18-26. ELSEVIER
    The Lancet Digital Health, 4(1), e18-e26. Elsevier Ltd
    The lancet / Digital health 4(1), e18-e26 (2022). doi:10.1016/S2589-7500(21)00211-9
    Kers, J, Bülow, R D, Klinkhammer, B M, Breimer, G E, Fontana, F, Abiola, A A, Hofstraat, R, Corthals, G L, Peters-Sengers, H, Djudjaj, S, von Stillfried, S, Hölscher, D L, Pieters, T T, van Zuilen, A D, Bemelman, F J, Nurmohamed, A S, Naesens, M, Roelofs, J J T H, Florquin, S, Floege, J, Nguyen, T Q, Kather, J N & Boor, P 2022, ' Deep learning-based classification of kidney transplant pathology : a retrospective, multicentre, proof-of-concept study ', The Lancet Digital Health, vol. 4, no. 1, pp. e18-e26 . https://doi.org/10.1016/S2589-7500(21)00211-9

    وصف الملف: application/pdf; Print-Electronic